As the significance of truck transportation in the modern economy continues to grow, the issue of fatigue driving in tanker trucks has garnered significant attention. Therefore, this study proposes a multimodal fatigue driving detection method. It involves conducting driving experiments with tanker trucks, collecting driving operation data, electrocardiogram data, and eye-tracking data. After data preprocessing, a multimodal driving dataset is generated. Data mining techniques are used to extract 42 driving feature values, and then, through correlation analysis, 27 feature values are selected for fatigue state detection. Subsequently, the K-means method is employed to classify driving data into four fatigue levels, and a random forest algorithm is used for fatigue state recognition. Experimental results demonstrate that the proposed fatigue detection algorithm achieves a precision rate of 92.6%, effectively identifying different fatigue driving states. This approach provides insights and theoretical support for subsequent driver risk assessment and targeted traffic management.


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    Titel :

    Fatigue driving state detection for tanker truck drivers based on multi-feature fusion


    Beteiligte:
    Yao, Xinwei (Herausgeber:in) / Kumar, Neeraj (Herausgeber:in) / Zhang, Ning (Autor:in) / Zhang, Ziyi (Autor:in) / Ma, Chuanyi (Autor:in) / Yang, Ziliang (Autor:in) / Zhang, Shengtao (Autor:in) / Wu, Jianqing (Autor:in)

    Kongress:

    Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) ; 2024 ; Beijin, China


    Erschienen in:

    Proc. SPIE ; 13160


    Erscheinungsdatum :

    16.05.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Freier Zugriff